1. bookVolume 9 (2017): Issue 47 (December 2017)
Journal Details
First Published
16 Apr 2017
Publication timeframe
4 times per year
access type Open Access

Models in Systems Medicine

Published Online: 16 Oct 2018
Volume & Issue: Volume 9 (2017) - Issue 47 (December 2017)
Page range: 429 - 469
Received: 05 Sep 2017
Accepted: 02 Nov 2017
Journal Details
First Published
16 Apr 2017
Publication timeframe
4 times per year

Systems medicine is a promising new paradigm for discovering associations, causal relationships and mechanisms in medicine. But it faces some tough challenges that arise from the use of big data: in particular, the problem of how to integrate evidence and the problem of how to structure the development of models. I argue that objective Bayesian models offer one way of tackling the evidence integration problem. I also offer a general methodology for structuring the development of models, within which the objective Bayesian approach fits rather naturally.


Adams, E. W. 1998. A Primer of Probability Logic. CSLI Publications, Stanford.Search in Google Scholar

Baumgartner, M.; and Gebharter, A. 2016. Constitutive relevance, mutual manipulability, and fat-handedness. British Journal for the Philosophy of Science 67(3): 731–56.10.1093/bjps/axv003Search in Google Scholar

Boogerd, F. C.; Bruggeman, F. J.; Hofmeyr, J.-H. S.; and Westerhoff, H. V. (eds.) 2007. Systems Biology: Philosophical Foundations. Elsevier, Amsterdam.Search in Google Scholar

Brigandt, I. 2013. Systems biology and the integration of mechanistic explanation and mathematical explanation. Studies in History and Philosophy of Biological and Biomedical Sciences 44(4A): 477–92.10.1016/j.shpsc.2013.06.002Search in Google Scholar

Carusi, A. 2014. Validation and variability: dual challenges on the path from systems biology to systems medicine. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 48: 28–37.10.1016/j.shpsc.2014.08.008Open DOISearch in Google Scholar

Casini, L.; Illari, P. M.; Russo, F.; and Williamson, J. 2011. Models for prediction, explanation and control: recursive Bayesian networks. Theoria, 26(1): 5–33.Search in Google Scholar

Clarke, B.; Gillies, D.; Illari, P.; Russo, F.; and Williamson, J. 2013. The evidence that evidence-based medicine omits. Preventative Medicine 57(6): 745–7.10.1016/j.ypmed.2012.10.020Search in Google Scholar

Clarke, B.; Gillies, D.; Illari, P.; Russo, F.; and Williamson, J. 2014a. Mechanisms and the evidence hierarchy. Topoi 33(2): 339–60.10.1007/s11245-013-9220-9Search in Google Scholar

Clarke, B.; Leuridan, B.; and Williamson, J. 2014b. Modelling mechanisms with causal cycles. Synthese 191(8): 1651–81.10.1007/s11229-013-0360-7Search in Google Scholar

Craver, C. F. 2007. Explaining the Brain. Oxford University Press.10.1093/acprof:oso/9780199299317.001.0001Search in Google Scholar

Danks, D. 2002. Learning the causal structure of overlapping variable sets. In Discovery Science: Proceedings of the 5th International Conference, ed. by S. Lange, K. Satoh and C. H. Smith, 178–91. Berlin. Springer.10.1007/3-540-36182-0_17Search in Google Scholar

Darwiche, A. 2009. Modeling and Reasoning with Bayesian Networks. Cambridge University Press, New York.10.1017/CBO9780511811357Search in Google Scholar

Dowe, P. 2000. Causality and explanation: review of Salmon. British Journal for the Philosophy of Science 51: 165–74.10.1093/bjps/51.1.165Open DOISearch in Google Scholar

Galas, D. J.; and Hood, L. 2009. Systems biology and emerging technologies will catalyze the transition from reactive medicine to predictive, personalized, preventive and participatory (P4) medicine. Interdisciplinary Bio Central 1(6): 1–5.10.4051/ibc.2009.2.0006Search in Google Scholar

Gammerman, A. (ed.) 1999. Causal models and intelligent data management. Springer, Berlin.10.1007/978-3-642-58648-4Search in Google Scholar

Glymour, C.; and Cooper, G. F. (eds.) 1999. Computation, Causation, and Discovery. MIT Press, Cambridge MA.Search in Google Scholar

Green, S. 2013. When one model is not enough: combining epistemic tools in systems biology. Studies in History and Philosophy of Biological and Biomedical Sciences 44(4A) :170–80.10.1016/j.shpsc.2013.03.012Open DOISearch in Google Scholar

Gruta, N. L. L.; and Turner, S. J. 2014. T cell mediated immunity to influenza: mechanisms of viral control. Trends in Immunology 35(8): 396–402.10.1016/j.it.2014.06.004Search in Google Scholar

Hoehndorf, R.; Dumontier, M.; Gennari, J. H.; Wimalaratne, S.; de Bono, B.; Cook, D. L.; and Gkoutos, G. V. 2011. Integrating systems biology models and biomedical ontologies. BMC Systems Biology 5(124) :1–16.10.1186/1752-0509-5-124Open DOISearch in Google Scholar

Illari, P. M.; and Williamson, J. 2012. What is a mechanism? Thinking about mechanisms across the sciences. European Journal for Philosophy of Science 2: 119–35.10.1007/s13194-011-0038-2Search in Google Scholar

Jaynes, E. T. 1957. Information theory and statistical mechanics. The Physical Review 106(4): 620–30.10.1103/PhysRev.106.620Search in Google Scholar

Koller, D. and Friedman, N. 2009. Probabilistic graphical models. MIT Press, Cambridge, MA.Search in Google Scholar

Kyriakopoulou, C.; and Mulligan, B. 2010. From systems biology to systems medicine. European Commission, DG Research, Directorate of Health workshop report. ftp://ftp.cordis.europa.eu/pub/fp7/health/docs/final-report-systems-medicine-workshop_en.pdf.Search in Google Scholar

Landes, J.; Osimani, B.; and Poellinger, R. 2018. Epistemology of causal inference in pharmacology: towards a framework for the assessment of harms. European Journal for Philosophy of Science 8(1): 3–39.10.1007/s13194-017-0169-1Open DOISearch in Google Scholar

Landes, J.; and Williamson, J. 2016. Objective Bayesian nets from consistent datasets. In Proceedings of the 35th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, volume 1757 of American Institute of Physics Conference Proceedings, ed. by A. Giffin and K. H. Knuth. Potsdam, NY.10.1063/1.4959048Search in Google Scholar

Lefaudeux, D. 2014. U-BIOPRED toolbox for fingerprint and handprint generation. Biomax Symposium 2014: Translating systems medicine into practice. http://www.biomax.com/symposium/2014/201409_lefaudeux.pdf.Search in Google Scholar

Leuridan, B. 2012. Three problems for the mutual manipulability account of constitutive relevance in mechanisms. British Journal for the Philosophy of Science 63(2): 399–427.10.1093/bjps/axr036Search in Google Scholar

Machamer, P.; Darden, L.; and Craver, C. 2000. Thinking about mechanisms. Philosophy of Science 67: 1–25.10.1086/392759Search in Google Scholar

MacLeod, M.; and Nersessian, N. J. 2013. Coupling simulation and experiment: The bimodal strategy in integrative systems biology. Studies in History and Philosophy of Biological and Biomedical Sciences 44(4A): 572–84.10.1016/j.shpsc.2013.07.001Open DOISearch in Google Scholar

McKim, V. R.; and Turner, S. 1997. Causality in Crisis? Statistical Methods and the Search for Causal Knowledge in the Social Sciences. University of Notre Dame Press, Notre Dame.Search in Google Scholar

Neapolitan, R. E. 2004. Learning Bayesian Networks. Pearson/Prentice Hall, Upper Saddle River NJ.Search in Google Scholar

Novere, N. L.; Hucka, M.; Mi, H.; Moodie, S.; Schreiber, F.; Sorokin, A.; Demir, E.; Wegner, K.; Aladjem, M. I.; Wimalaratne, S. M.; Bergman, F. T.; Gauges, R.; Ghazal, P.; Kawaji, H.; Li, L.; Matsuoka, Y.; Villeger, A.; Boyd, S. E.; Calzone, L.; Courtot, M.; Dogrusoz, U.; Freeman, T. C.; Funahashi, A.; Ghosh, S.; Jouraku, A.; Kim, S.; Kolpakov, F.; Luna, A.; Sahle, S.; Schmidt, E.; Watterson, S.; Wu, G.; Goryanin, I.; Kell, D. B.; Sander, C.; Sauro, H.; Snoep, J. L.; Kohn, K.; and Kitano, H. 2009. The systems biology graphical notation. Nature Biotechology 27(8): 735–41.10.1038/nbt.1558Search in Google Scholar

O’Malley, M. A.; and Soyer, O. S. 2012. The roles of integration in molecular systems biology. Studies in History and Philosophy of Biological and Biomedical Sciences 43(1): 58–68.10.1016/j.shpsc.2011.10.006Open DOISearch in Google Scholar

Parkkinen, V. P.; Wallmann, C.; Wilde, M.; Clarke, B.; Illari, P.; Kelly, M.P.; Norell, C.; Russo, F.; Shaw, B.; and Williamson, J. 2018. Evaluating Evidence of Mechanisms in Medicine: Principles and Procedures. Springer.10.1007/978-3-319-94610-8Search in Google Scholar

Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo CA.10.1016/B978-0-08-051489-5.50008-4Search in Google Scholar

Pearl, J. 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge.Search in Google Scholar

Russo, F.; and Williamson, J. 2007. Interpreting causality in the health sciences. International Studies in the Philosophy of Science 21(2): 157–70.10.1080/02698590701498084Search in Google Scholar

Salmon, W. C. 1984. Scientific Explanation and the Causal Structure of the World. Princeton University Press, Princeton NJ.10.1515/9780691221489Search in Google Scholar

Sobradillo, P.; Pozo, F.; and Agusti, A. 2011. P4 medicine: the future around the corner. Archivos de Bronconeumologia 47(1): 35–40.10.1016/S1579-2129(11)70006-4Search in Google Scholar

Spirtes, P.; Glymour, C.; and Scheines, R. 1993. Causation, Prediction, and Search. MIT Press, Cambridge MA, 2nd edition, 2000.Search in Google Scholar

Tillman, R.; Danks, D.; and Glymour, C. 2008. Integrating locally learned causal structures with overlapping variables. In Advances in Neural Information Processing Systems, volume 21, ed. by D. Koller, D. Schuurmans, Y. Bengio and L. Bottou, 1665–72. La Jolla, CA. The NIPS Foundation.Search in Google Scholar

Tillman, R.; and Spirtes, P. 2011. Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, ed. by G. Gordon, D. Dunson and M. Dudík, 3–15. Fort Lauderdale, FL. Journal of Machine Learning Research 15. http://jmlr.csail.mit.edu/proceedings/papers/v15/.Search in Google Scholar

Vandamme, D.; Fitzmaurice, W.; Kholodenko, B.; and Kolch, W. 2013. Systems medicine: helping us understand the complexity of disease. Quarterly Journal of Medicine 106(10): 891–5.10.1093/qjmed/hct163Search in Google Scholar

Wilde, M.; and Williamson, J. 2016. Models in medicine. In Routledge Companion to Philosophy of Medicine, ed. by M. Solomon, J. Simon and H. Kincaid, 271–84. Routledge: New York and London.Search in Google Scholar

Williamson, J. 2005a. Bayesian Nets and Causality: Philosophical and Computational Foundations. Oxford University Press: Oxford.10.1093/acprof:oso/9780198530794.001.0001Search in Google Scholar

Williamson, J. 2005b. Objective Bayesian nets. In We Will Show Them! Essays in Honour of Dov Gabbay, volume 2, ed. by S. Artemov, H. Barringer, A. S. d’Avila Garcez, L. C. Lamb and J. Woods, 713–30. College Publications, London.Search in Google Scholar

Williamson, J. 2010. In Defence of Objective Bayesianism. Oxford University Press, Oxford.10.1093/acprof:oso/9780199228003.001.0001Search in Google Scholar

Williamson, J. 2013a. How can causal explanations explain? Erkenntnis 78: 257–75.10.1007/s10670-013-9512-xOpen DOISearch in Google Scholar

Williamson, J. 2013b. Why frequentists and Bayesians need each other. Erkenntnis 78(2): 293–318.10.1007/s10670-011-9317-8Open DOISearch in Google Scholar

Williamson, J. 2017. Lectures on Inductive Logic. Oxford University Press: Oxford.10.1093/acprof:oso/9780199666478.001.0001Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo